Computational Fluid Dynamics (CFD) simulations are used to predict the fluid behavior and particle systems-in-action in three-dimensional space, allowing experts to evaluate a series of environmental decisions in designing buildings. Although computing power has increased in the past decade, detailed CFD simulations introduce time-delay that defeats the notion of real-time data visualization. A method that can bypass the time-consuming simulations and generate results comparable to detailed CFD simulations will allow such visualizations to be constructed. This paper discusses a pilot project that utilizes a Reinforcement Learning (RL) algorithm coupled with a simplified fluid dynamics equation to generate thermal performance data for real-time visualization.